[PR #986] [MERGED] Advanced examples #1108

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opened 2026-02-28 00:03:39 +03:00 by kerem · 0 comments

kerem commented

2026-02-28 00:03:39 +03:00

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📋 Pull Request Information

Original PR: https://github.com/spotipy-dev/spotipy/pull/986
Author: @arefjadda
Created: 6/9/2023
Status: ✅ Merged
Merged: 7/9/2024
Merged by: @stephanebruckert

Base: master ← Head: advanced_examples

📝 Commits (6)

d77c953 Created a testing suite for the spotipy client.Spotify class
fcb3dd4 Delete pyvenv.cfg
3e5c5db Corrected error in description
5f3d0bb Added some advanced usage examples, some of which involve utilizing basic data analysis methods using other libraries such as Pandas
2e49feb Minor formatting fixes
2319ed6 Merge branch 'master' into advanced_examples

📊 Changes

4 files changed (+132 additions, -0 deletions)

View changed files

📝 CHANGELOG.md (+1 -0)
➕ examples/audio_features_analysis.py (+33 -0)
➕ examples/personalized_playlist.py (+53 -0)
➕ examples/track_recommendations.py (+45 -0)

📄 Description

Added three new examples:

audio_features_analysis.py: This example focuses on leveraging Spotipy's audio features. It demonstrates how to fetch audio features of tracks from a playlist and perform analysis using libraries like pandas and seaborn. Users can explore correlations between different audio features and visualize the results.
personalized_playlist.py: This example demonstrates how to create a personalized playlist. It showcases the process of fetching tracks based on user preferences, selecting and clustering tracks using k-means clustering, and creating a new playlist with the selected tracks.
track_recommendations.py: This example showcases how to utilize the track recommendation feature. It guides users through the steps of retrieving recommendations based on their listening history or liked tracks, and provides guidance for generating recommendations.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/spotipy-dev/spotipy/pull/986 **Author:** [@arefjadda](https://github.com/arefjadda) **Created:** 6/9/2023 **Status:** ✅ Merged **Merged:** 7/9/2024 **Merged by:** [@stephanebruckert](https://github.com/stephanebruckert) **Base:** `master` ← **Head:** `advanced_examples` --- ### 📝 Commits (6) - [`d77c953`](https://github.com/spotipy-dev/spotipy/commit/d77c953a72cc3728f67649022408a73f28f47aae) Created a testing suite for the spotipy client.Spotify class - [`fcb3dd4`](https://github.com/spotipy-dev/spotipy/commit/fcb3dd4e9a47dd5f500872f2490e96d47bfd8a63) Delete pyvenv.cfg - [`3e5c5db`](https://github.com/spotipy-dev/spotipy/commit/3e5c5dbe2020254e862ff4778f5927343fe7ee22) Corrected error in description - [`5f3d0bb`](https://github.com/spotipy-dev/spotipy/commit/5f3d0bb578f388ca8fed102226a2fa382b392379) Added some advanced usage examples, some of which involve utilizing basic data analysis methods using other libraries such as Pandas - [`2e49feb`](https://github.com/spotipy-dev/spotipy/commit/2e49feb5fc13bdc448c1cba06e76f29019d139e6) Minor formatting fixes - [`2319ed6`](https://github.com/spotipy-dev/spotipy/commit/2319ed69fdc2acd8a9f95e8229256e0afd3b6d7d) Merge branch 'master' into advanced_examples ### 📊 Changes **4 files changed** (+132 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `CHANGELOG.md` (+1 -0) ➕ `examples/audio_features_analysis.py` (+33 -0) ➕ `examples/personalized_playlist.py` (+53 -0) ➕ `examples/track_recommendations.py` (+45 -0) </details> ### 📄 Description Added three new examples: - audio_features_analysis.py: This example focuses on leveraging Spotipy's audio features. It demonstrates how to fetch audio features of tracks from a playlist and perform analysis using libraries like pandas and seaborn. Users can explore correlations between different audio features and visualize the results. - personalized_playlist.py: This example demonstrates how to create a personalized playlist. It showcases the process of fetching tracks based on user preferences, selecting and clustering tracks using k-means clustering, and creating a new playlist with the selected tracks. - track_recommendations.py: This example showcases how to utilize the track recommendation feature. It guides users through the steps of retrieving recommendations based on their listening history or liked tracks, and provides guidance for generating recommendations. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>